Ag18, phenols, and Ag18-phenol complex moieties were optimized in air at the B3LYP level of theory using Gaussian 09 software. Ag-18 was preferred in this work due to its molecule-like properties. Previously, [21] reported that Ag-18 is preferred due to its unique bonding properties within the metal core. The specific and atomic level structure of the Ag-18 also provides a significantly higher antimicrobial efficiency and better stability as compared to other nanoclusters and conventional silver nanoparticles. Using mass spectrometry, [22] demonstrated that Ag-18’s atom-precise design (<2 nm) yields superior geometric rigidity, with minimal core fragmentation. Figure 1Figure 1 shows the optimized structure of the Ag-18 nanocluster and that of phenols. As indicated in Figure 1Figure 1, phenols have a single hydroxyl group with an oxygen atom that is electronegative and which will play a key role in the adsorption process.

Figure 1. Optimized structure of Ag-18 nanocluster (left) and phenols (right).

As shown in Figure 1Figure 1, Phenols have a hydroxyl (OH) group that is directly attached to the benzene ring, replacing one of the hydrogen atoms. In this work, the 3D molecule was directly imported from PubChem before being optimized using Gaussian 09.

Raman and SERS spectral characterization of phenols was carried out to ascertain the possible changes in band position after phenols are adsorbed on silver nanoparticles. In the Gauss View window, phenols were allowed to embed on the metallic nanoclusters with the oxygen atom of phenols being the target atom in the attachment, as shown in Figure 2Figure 2. Oxygen has previously been reported by [23] to be the most electronegative and, as such, would help to form a stronger bond during adsorption.

Figure 2. Silver nanoclusters embedded on phenolic compound in Gauss view.

Figure 3. Simulated Raman and SERS spectra of phenols (inset: spectra of silver NPs).

The results depicted in Figure 3Figure 3 show that phenols exhibit very strong Raman bands within the active spectral window between 600 cm⁻¹ and 1700 cm⁻¹ due to the symmetric stretching mode of the aromatic ring (C = C). Some of the bands between 1100 cm⁻¹ and 1300 cm⁻¹ tend to be isoenergetic, with almost identical spectral features. The simulated spectra of phenols were found to be similar to those reported by other authors for both simulated and experimental data. For instance, [24] noted that two ring deformation modes around 1500 - 1600 cm⁻¹ offer a potential route in gaining information about phenolic compounds in the macroenvironment. Density functional theory predictions given by [25] also showed that phenols have predominant Raman scatter bands at 850 cm⁻¹, 1120 cm⁻¹, 1291 cm⁻¹, and 1560 cm⁻¹ assigned to C = C and C = O symmetric vibrational bonds using vibrational energy distribution (VEDA).

Adsorbing phenols on silver nanoparticles had notable implications on the phenols’ Raman bands. As noted in Figure 2Figure 2, some of the bands reported higher signal intensity after adsorbing the molecule on silver nanoparticles. The peaks at 1120 cm⁻¹, 1291 cm⁻¹, and 1345 cm⁻¹ reported an almost similar threefold enhancement factor. [26] Defines the enhancement factor as the key parameter for the assessment of the substrate performance. In this case, the signal enhancement factor was given as the ratio of the enhanced signal to the non-enhanced signal for a specific Raman scatter band. The enhancement factor directly quantifies the ability of silver nanoparticles as a substrate that can amplify phenol molecule signals for trace-level contaminant detection [27].

The other observation made from Figure 2Figure 2 is the shift in some of the Raman scatter bands. The bands that reported a red shift after phenols are adsorbed on the silver nanoparticles include 1120 cm⁻¹, 1291 cm⁻¹, 1561 cm⁻¹, and 1669 cm⁻¹. All these shifts were characteristic of the C = C and C = O stretching vibrations, while 1345 cm⁻¹ (C-O breathing vibrational mode) had a slight blue shift after adsorption. Because these modes are structurally specific, even slight band position changes can indicate that phenols are present and interacting with their microenvironment in water [28]. [29] Also argues that tracking these shifts as a function of time or concentration enables early detection and even speciation of phenolic contaminants.

Silver nanoparticles were used both as substrates (in SERS) for signal enhancement and spectral shifts characterization, and as nanostructures for localized surface plasmon resonance sensing of phenols. The plasmon band of silver nanoparticles presents a strong optical effect for collective oscillations, such that by monitoring the LSPR peak shift (change in wavelength) caused by the binding material, it is possible to achieve a label-free detection of the analyte. In this work, the high refractive index sensitivity of silver nanoparticles was monitored by adsorbing phenols on silver nanoparticles and then measuring the change in the LSPR position using simulated absorbance spectra, as shown in Figure 4Figure 4.

Figure 4. UV-VIS spectra of Silver Nanocluster (Ag18) and phenols/AgNCs complex. Inset is the absorbance spectra of phenols.

As shown in Figure 4Figure 4, phenols have two absorbance bands at 270 nm and 275 nm. The simulated band was similar to what has been reported by other scholars, such as [30]-[32]. Therefore, the UV-VIS calculations implemented using DFT reproduced the experimental results by almost 100% as reported by other scholars. Secondly, the absorbance band of silver nanoclusters was 462 nm, which is almost similar to what was reported by other scholars. [33] Reported the plasmon band at 450 nm using an experimental procedure, while [34] used DFT and estimated the absorbance band of silver nanoparticles to be 426 nm. The deviations in the absorbance values reported for this work could be attributed to the differences in the number of atoms used in the nanoclusters.

As noted in Figure 4Figure 4, there is a slight redshift in the localized surface plasmon resonance band of the silver nanocluster from 462 nm to 478 nm. The +16 nm change is significant in environmental monitoring applications, as explained by [35]. According to [36], silver nanoparticles are more sensitive to changes in both the local and bulk environment. Introducing phenols to silver nanoclusters changes the refractive index of the local environment, a move that allows label-free trace and ultra-trace detection of phenols and other contaminants in the environment. The results reported in Figure 4Figure 4 show that silver nanoparticles are very sensitive to any changes in the optical properties of the surrounding medium, like the adsorption of the target analyte (phenols) on the nanomaterials. Therefore, the silver nanoparticles are important candidates for monitoring phenols within the environment.

In this part, Density functional theory was employed as a synergistic approach that provides a molecular-level understanding of the electrical and optical properties of silver nanoclusters when adsorbed with phenols. The focus was on understanding the adsorption sites and the electron transfer, both of which are key in elucidating the adsorption process of phenol removal from water. [37] Leveraged the density of state, which reflects the density of kinetic energy, to ascertain the confinement and scattering of charge. Density of states (DOS) was used to describe how many quantum states are available at each energy in a system, and it is a central bridge between electronic structure, spectroscopy, and adsorption phenomena. DOS was evaluated before (Figure 5Figure 5) and after adsorption (Figure 6Figure 6) to ascertain changes in the band gap energy, which is also an important parameter in biosensing applications.

Figure 5. Density of state spectra of phenols.

Figure 6. Density of state spectra of phenols adsorbed on AgNPs.

As shown in Figure 6Figure 6, there is a general reduction in the band gap energy after phenols are adsorbed on silver nanoparticles. The band gap reduced from approximately 7 eV to less than 2 eV. [38] Asserts that a reduction in the band gap after adsorption enhances a material’s light absorption range and charge-carrier generation, enabling more sensitive optical detection of environmental analytes, such as water pollutants, through spectroscopic changes. In sensing applications, the narrowing of the band gap also translates to improved sensor sensitivity by amplifying optical responses to low analyte levels, as even trace adsorption triggers detectable spectral changes. The observations in this case are critical to developing a label-free analytical technique for monitoring environmental pollutants.

Table 1. Mulliken charges for selected elements for phenol molecules adsorbed on Ag18.

The interaction between silver and phenols was also explained with the help of atomic charge analysis. [39] Notes that the Mulliken charge is directly related to the vibrational properties of the molecule and also quantifies how the electronic structure charges under atomic displacement. Therefore, using the charge distribution, it is possible to underscore the chemical bonds present in the molecule. Table 1 shows the Mulliken charges of some of the carbon, hydrogen, and oxygen atoms for phenols and those of silver atoms during the adsorption process at a specific level of theory.

All the Carbon and hydrogen atoms in the molecule have positive charges, while some silver atoms and oxygen show negative charges, with oxygen being the most electronegative. Figure 7Figure 7 shows a summary of the charge distribution.

Figure 7. Mulliken charge distribution for Phenols adsorbed on silver nanocluster.

The Mulliken charge distribution shows that oxygen is the most electronegative atom, giving it the highest tendency to attract and bond to the electropositive silver during the adsorption process. Therefore, functional groups with an oxygen molecule will play an important role in defining the interaction between silver and oxygen during the adsorption process.